Archives for Chris

I have been involved in recent discussions on structured project management methodologies. In particular PRINCE2 (which I’ll focus on here but the discussion could apply to any traditional project management approach). Are traditional methodologies like PRINCE2 compatible with a lean or agile way of working?

There are plenty of blogs discuss the experience of making PRINCE2 and lean-agile ways of working fit together and it’s certainly possible. My issue is not that PRINCE2 is wrong. It’s more that, for a IT development project, it encourages us to put our focus in the wrong place.

Discovery

Lean-agile ways of working emphasis the discovery nature of IT development. The tricky thing about projects involving IT development is that they are characterised by large uncertainties in both what is needed and how to build it. In this way, it’s more like other creative disciplines like marketing, R&D etc. where progress cannot be pictured as a linear function of the resources applied over time. We could call this the discovery mindset:

the customer doesn’t know what they want,

the developer doesn’t know how to build it,

things change.

IT development projects do have one redeeming feature – they can normally be delivered in small pieces which enables discovery to be done collaboratively in short learning cycles whereby we find out what is needed and how to build it by trying a bit in a short cycle.

Risk and Learning

PRINCE2 implies that the risks that need most of our attention are cost, time and scope overruns. Not really. The biggest risk in IT development is building something which isn’t used (ref. Mary Poppendieck) What needs our attention most is answers to the question of how we can learn faster about whether our solution will be used. Thick contractual requirements documents which are produced upfront, beloved of so many PRINCE2 practitioners, actually increase the risk that we build something that isn’t used since they increase the amount of work done before we find out.

Making learning our primary focus has all sorts of implications. PRINCE2 implies that change happens rarely and need to be strictly controlled. Yet if we are learning all the time, change will happen, well, all the time. Lean-agile practices embraces change and treats it as “business and usual”. Take retrospectives – these happen often, unlike the PRINCE2 lessons learnt report which happens only at the end. Or planning. This is a core activity in the PRINCE2 world, yet planning is merely helpful in the lean-agile. Planning is of limited value because there is always so much we don’t know yet – we typically can plan quite well the next couple of weeks but beyond that it gets vague. We need to iterate on our plan as we learn more.

Flow

PRINCE2 typically has no real concept of flow beyond the level of the GANT chart. Usually, each step of the GANT chart is executed only once. Lean-agile practitioners frame a project more in terms of setting up a production pipeline and then flowing small work items smoothly through it. This allows for adjustment and learning – it becomes a repeat game and we don’t have to get it right first time. Lean-agile thinking acknowledges that output actually goes up if a team is not working on too much at any one time. As such, lean-agile teams tend to pull work into the pipeline when there is capacity (and not when the GANT chart says they should. As Scott Ambler says “friends don’t let friends use Microsoft Project”).

Managing smooth flow (i.e. not having work piling up someplace; in testing, for example) also has implication for recruitment. Lean-agile teams tend to value people who are multi-skilled since they can move to where the bottleneck is building up in the pipeline – the location of which is impossible to predict in advance. PRINCE2 practitioners tend to prefer engaging specialists as these are the most effective at particular tasks that have been planned for them to do.

Value

If we are good at prioritisation, we might learn at some point that we have implemented 80% of the value for 20% of the cost and want to stop the project now. Scope is typically variable in a lean-agile project. PRINCE2 tries to nail down scope, cost, quality up front since it assumes these can be understood well enough before work starts – a questionable assumption for IT development.

Quality

PRINCE2 has a very contractual definition of quality. If we could usefully specify quality contractually in IT development e.g. “1 bug per 1000 lines of code”, we would. Alas not! Lean-agile thinking addresses this in a more practical way – quality is in a lean-agile context is about getting quick and complete feedback on our activities which then allow us to adjust and improve – quality is built into the process itself.

Collaboration

Stakeholder management in PRINCE2 is “contractual” with clearly defined roles etc. Lean-agile thinking focuses more on collaboration, face-2-face communication, joint problem solving etc. which is some way from the formal mindset of PRINCE2. It also typically emphasises self-organising teams since the team is closest to the work and hence are the ones who are learning the most about who is best to do what.

Recommendations

So here’s my top five points for a PRINCE2 project manager who wants to maximises his/her chances that an IT development project will deliver value:

Make your primary focus to enable fast learning everywhere in your project (particular about whether the solution is actually used. Get started on this immediately by getting the smallest chunk of value possible in front of them straight away). Learn both about the customer needs and how to build the solution. Be fanatical about this.

Frame the project as quickly setting up a “production pipeline” through which there is a smooth, fast flow of small requirements.

Be OK with not pretending you know too much about the future. Educate your stakeholders as to why this makes sense.

Charter your team to deliver as much value as possible with a given timeframe/cost and the let them work out how to do this.

The Key Perfomance Indicators(KPI)/Measurable Objectives setting process triggers the discussion as to whether the way KPI’s are done within a company adds or destroys value. A key plank of a lean-agile approach is systems thinking i.e. focus on optimising the whole end-2-end process, not the individual parts which are KPI’s often do. Deming, who you might say is father of this way of thinking, was specifically against the way to do KPIs. Number 12 in his 14 key principles is:

Remove barriers that rob people in management and in engineering of their right to pride of workmanship. This means, inter alia, abolishment of the annual or merit rating and of management by objective

Enough! It’s easy to criticize KPIs – it’s better to improve. Here’s a summary of the usual suspects and how they can be improved upon.

Variable

Typical measure

Usual outcome

Alternative measures

Time

Delivering on a predicted date

Incentivises hidden time buffers and slower delivery

Maximise speed in getting to the point where value starts to be realised

Scope

Delivering all of the originally predicted scope

Incentivises gold plating and discourages exploitation of learning.

Minimize size of work packages to maximize both learning and early release of value

Cost

Delivering at or below a predicted development cost

Incentivises hidden cost contingencies, pushing costs up.

Maximize value delivered (trade development cost against the opportunity cost of delay)

Quality

Delivering changes with zero downtime and no errors

Fear of change. Overinvestment in testing and documentation.

Shorten feedback cycles at many levels (coding, defects…)

In short, the suggestion is that by over-focussing on the typical measures in the table above, we get a pipeline which is slow, expensive and wasteful. Explore the alternative measures instead!

Try…

Perhaps the table above can be used for inspiration when setting KPIs in your team?

Don’t we just get frustrated when our estimates or guesstimates on timescales or costs turn out to be different from what actually happens? So how do we improve the accuracy of our estimates?

What do we need estimates for?

This is a golden question – ask yourself this whenever you do an estimate. It’s a lean principle to remove waste (i.e. non-value adding activity). If the estimate information you are producing isn’t vital for making a decision, then take a good hard look as to whether you need it. Some of the teams I have worked with have stopped some kinds of estimates as these estimates were not changing the decision.

Why are estimates uncertain?

Estimates are uncertain because we lack information. We’ve never built this particular feature before. We don’t exactly know what we need to do. As we work further with the feature, we gain more information and our uncertainty about how much work is required reduces. This leads to the idea of the cone of uncertainty shown below which suggests that uncertainty decreases (and hence accuracy of estimate increases) throughout the life of a feature.

What helps improve estimating accuracy?

1. Break work down

If you only do one thing, break the work down into smaller pieces (this is a core lean principle!) It’s much easier to estimate smaller activities than larger ones. By simply breaking down large work items into smaller pieces, your estimating accuracy will improve.

2. Increase the rate at which the estimator gains experience

Ability to estimate comes from learning from past experience of estimating. So get more experience! The way you increase your learning rate is to shorten the cycle time i.e. the time it takes to produce something. Doubling the cycle time doubles the rate of learning! Of course, the experience gained needs to be effectively captured (retrospectives, low staff turnover, etc.).

3. Ensure estimators are the people who are doing the work

Seems simple but in a lot of cases, the people doing the estimating are far from the actual work (or, sometimes, estimates are adjusted by people far from the work!)

4. Reduce dependencies

Typically if a task requires 90% of one person and 10% of another, the 10% person will be the bottleneck because his focus is not on the task. This means that it’s difficult for him/her to be available precisely when needed. Eliminating this dependency will reduce variation in timelines. Feature teams help with this.

5. Get multiple inputs

Agile teams often use planning poker to harness the brainpower of the entire team to improve the estimate – it’s unlikely that one person has all the best information. This is a fun and easy thing to try.

6. Agree on which estimate we want

Many timeline estimates are simply the earliest possible time for which the probability of delivery is not zero. Did you want the most likely date (50% chance of being early, 50% chance of being late) or the date for which it is almost certain delivery will have happened by? Make sure everybody agrees which estimate we are talking about.

7. Understand the politics

There are also all sorts of human dynamics that can skew your estimate. For example:

If there are “punishments” for getting it wrong, folk tend to add buffers to their estimates.

If folk believe that the uncertainty associated with the estimate will not be communicated clearly (for example to senior management) then they tend to add buffers. A solution to this that a lot of agile teams use when they are doing an early guesstimate is to use “T-shirt” sizing whereby the estimate is classified as Small, Medium, Large or Extra Large. Communicating guesstimates like this makes it clear that they are not very accurate.

If there is already a target declared (“we want this to be under $100k” or, “we want to complete this by June”) then folk tend to be anchored by that and don’t like to say it’s not possible. We all like to please our stakeholders.

Many fascinating studies show that people have a natural subconscious tendency to be overconfident in their estimating skills. If the estimator(s) have little data on past performance to help correct this, then its likely they will underestimate tasks.

Setting expectations on estimate accuracy

It’s tempting to set a team a goal to improve estimate accuracy. The risk with this (which I have seen in some teams) is that the quickest and simplest way of improving estimating accuracy is either to add undeclared buffers or to invest more time and resource before making the estimate. The latter effectively moves estimate creation to later in the lifecycle which destroys the purpose of the estimate (which is to make decisions early on in the lifecycle).

A better way of improving estimating accuracy is to set the team goals on reducing cycle time and reducing the size of items flowing thro’ the pipeline (as per the first two points above). By focusing on these general lean-agile practices, we also get an improvement in estimating accuracy!

PM: “So there is nothing which happens at midnight on 30th June which means that if we deliver a minute later then this really matters. It is more like you’d like this to happen earlier than later?”

SE: “Yes, that’s right.”

PM: “I understand that it’s top priority. It will help if we can characterising what top priority means since there are always other projects who are claiming to be top priority. The business case says that this change will have a benefit of $52m per year which is the same as $1m per week. Does that mean that for every week we are able to get this project done early, the company would realise an extra $1m?”

SE: “Yes, that’s true in this case.”

PM: “Does that mean we’d be willing to spend up to $1m to get the project a week earlier?”

SE: “I never thought of it like that but, yes.”

PM: “Great. Now we can make our case about being top priority. Every time we have to wait in a queue for some bottlenecked resource that is on our critical path, we take the amount we have to wait (in weeks) and multiply by $1m and this is the opportunity cost to the organisation of us not going first!”

SE: “I like the sound of that!”

PM: “Of course, the other projects will calculate the opportunity cost they incur if we go first. We will compare opportunity costs and the biggest will win.”

SE: “Em. I suppost that makes sense. Although I don’t like the idea of waiting.”

PM: “That I can understand. We are trying to to what’s right for the company overall. This calculation (called CD3 = cost-of-delay divided by duration or weighted shortest job first) ensures this.”

SE: “Yes. You’re right.”

PM: “Going back to the $1m per week. If we agree on this figure, and the team identify something they can do to bring the delivery forward by a week that costs less than $1m, do we have the authority to spend the money to make this happen?”

SE: “I’m not sure. They can always escalate to me if necessary.”

PM: “So you agree to make yourself available quickly if we have something we need to escalate.”

SE: “Well I am very busy and important.”

PM: “Yes. If you are unable to make the time and unwilling to delegate then the cost to the company will be $1m per week.”

SE: “Yes. You are right. I will be available.”

PM: “With regards to the delivery dates. Our estimated date was arrived at by harnessing the brainpower of the whole team – the people who are closest to the work. This is the best estimate we can get with the information we have today – there is no better way.

At $1m per week, I understand the urgency of realising value as soon as possible, so we will set up our delivery pipeline in order to break work down into small chunks of value and then realise that value, starting with the most valuable chunks. Using this approach, you will see that the team is delivering value before end of June and you will also have ongoing opportunities to change the priorities as we go and we learn more.

This also allows us to check assumptions about the value of the solution and how to build it.”

SE: “I don’t understand. Can’t you just double the team size and deliver earlier?”

PM: “In any process, it only makes sense to add resource at the process bottleneck. Adding resource anywhere else will slow the team down. IT development is a complex human intensive process. If there are 10 people on the team and we add one more, then there is an overhead on the original 10 in bringing the 11th up to speed, maintaining communication and alignment etc. This is particularly true when the team processes are new. Until there is a running process that is demonstrably creating value then it doesn’t make sense to scale the process and add lots more people.”

SE: “Can’t we just start a lot of work in parallel and have a lot of different teams?”

PM: “Yes this would be possible if the nature of the work was such that there were no dependencies between the teams but the dependencies we have will create bottlenecks. In any case, the most scarce resource in any organisation is usually management attention. Its better to do a smaller number of activities and work on doing them fast than a large number at once and do them all slow.”

SE: “You said that you want a running process that is demonstrably delivering value. When will that be?”

PM: “We aim to begin realising business value within 30 to 90 days.”

SE: “We’ll that is sooner than I expected and I guess I’ll see whether the approach you propose is working by then. I’m looking forward to that.”

Maybe I’m a dreamer but if only all these conversations went like this…

Which is most important? To ensure our teams are fully busy or to maximise the value delivered by the development pipeline? I’m hoping to have you consider that there may be times in when it is in the company’s interest to pay people to “look out of the window” for periods of time.

Throughput decreases as capacity utilisation approaches 100%

Think about a highway – cars flow fine when there is little traffic. When the volume of traffic goes above 80%, we start to get traffic jams. As the loading on the highway approaches 100% then we end up almost no flow at all. The overall value delivered (i.e. people getting to where they want to go) is less. Models have been developed which give the underlying maths of why this is. Highway designers have actually taken these model’s to heart and there are now places were cars are held by traffic lights on the sliproad at peak times in order to maintain a smooth flow on the main highway. The funny thing is this improves everybody’s end-2-end journey time including the cars which were held on the sliproad for a period. Its counter-intuitive, as it probably doesn’t feel like that when you are sitting on the sliproad not going anywhere.

A development pipeline has similar characteristics to a highway. The inherent variation in both demand and supply means that throughput is maximised when the pipeline is run on average below full capacity.

An example

One of the teams I coach is effectively doing a whole release which is dominated by a technical upgrade. To simplify the situation, what that means is that it requires the developers and testers but not the analysts. What should the analysts do whilst the developers/testers are busy with this release?

Options are:

Worst option: Do more analysis. Since the bottleneck is not analysis work, all this will do is pile up a lot of work for the developers/testers to work on. This pile will probably never disappear because as soon as the developers remove something from it, the analysts will continue adding a new item. We haven’t increased the value generated by the pipeline but we’ve increased the cycle time and hence decreased agility and speed of feedback/learning. Think of all the management and communication that with be required to look after this pile of half finished work? The overall value generated is almost certainly negative.

Better option: Analysts look out the window for a while. This has no negative effects on the value delivered. Of course, if this is a permanent thing (which in this case it isn’t) we should look into reducing the number of analysts.

Best option: Analysts learn over time how to help with development and test. They may not be the most efficient at these tasks. T-shaped individuals can move to the bottleneck and increase it’s capacity. This would enable the technical release to be ready earlier and hence increases the value delivered by the pipeline.

It’s counter-intuitive for us to accept that there are situations where the most value people can generate in the short-term is to do nothing. We all like to be busy. It’s not about the productivity of the individual but the productivity of the pipeline as a whole. What do you think?

A common problem in increasing the flow of work through a development pipeline is over-specialisation. The bottleneck might be in getting the analysis done, yet we have plenty of developers who have nothing to do since they have no analysis skills. Then the bottleneck moves to the front-end development yet we discover we have plenty of back-end developers but none who know how to code the front-end. And so on. Enter the T-shaped individual which is a common lean-agile practice applied to address this issue and increase throughput through the development pipeline.

What is a T-Shaped Individual?

A T-shaped individual is not the same as a generalist. He or she has deep expertise in one area but is able and willing to turn his/her hand to other things.

How can I promote this approach?

Teams I have worked with that have been committed to this practice have found the following helpful:

Hire people who want to develop outside their specialism An expert in, say, workflow, is not going to get an opportunity to learn just about workflow. In developing T-shaped individuals, the learning opportunities might be in rules or service bus, business analysis, performance testing etc. New hires have to want this.

Organise around the flow of value If the team are not organised around the flow of value but instead around developing components which are only part of the whole, then there will be little opportunity to develop skills outside one’s own specialism.

Actively manage the skill map One way of doing this is to identify all the different skill areas required to produce value. Then the entire team score themselves against this in one of 4 ways. This skill map is then used helping the team decide who does what and for managing skill development. This also enables team members to learn and develop in the areas that interest them and helps with minimising dependencies on key individuals.

I can teach others

I can do the job with help from others

I can do the job alone

I am eager to learn

Fig. 1. A simple rating of skill level

Try…

Would T-shaped individuals help improve the throughput of your delivery pipeline? Are you willing to make any necessary changes to your hiring practice and organisation?

Retrospectives are a practice used by a lot of software teams – particularly popular with those using SCRUM, since the “sprint retrospective” is part of the SCRUM methodology. They are a effective and simple framework for continuous improvement (Kaizen) for any team – not just those delivering software.

How retrospectives work

The team meets to look at what they have been doing and what they can do differently going forward. Mindset is important – in order for team learning to be effective, there is no room for finger-pointing. The retrospective prime directive captures this:

“Regardless of what we discover, we understand and truly believe that everyone did the best job they could, given what they knew at the time, their skills and abilities, the resources available, and the situation at hand.”

Frequency of retrospectives

Many teams organise a “lessons learnt” session after the project is done (or after a major release). This is too late! It’s too late to change anything, some events have been forgotten and energy levels are low. Retrospectives need to be scheduled at least monthly.

How retrospectives work in our coaching team?

We “eat our own dog food” so we have always had monthly retrospectives in the coaches team. For reasons that are lost in the mists of time (creating a relaxed environment maybe?) we always hold our retrospectives in a bar. The facilitator of the session is not our team leader (me) but is the same as the person who is facilitating our team kanban board (a role which rotates so that all team members get to do it sooner or later).

As a warm up, we create a timeline of the key events which have happened since the last retrospective – handy to bring what’s been going on to the front of our minds:

We then answer the 4 key questions – there are other possible sets of questions out there which we have experimented with (e.g. the retrospective starfish) but these four seem to work best for us:

What did we do well, that if we don’t discuss we might forget?

What did we learn?

What should we do differently next time?

What still puzzles us?

We are very low tech – we have one sheet of A3, divided into four areas, which we all write on at the same time.

When we are all finished writing, we go fairly slowly through everything everybody has written and agree any actions which we transfer to our team kanban board. The whole thing normally takes us about 2 hours – we’ve tried to do it in less but it just hasn’t worked out for us.

So, lets cut to the chase. What have we got of value out this? I’d say two things. Firstly the concrete actions we take as a result of the retrospective have improved the way we work. This has been in many, many small ways. Also, by including the whole team, we have perhaps identified earlier than we otherwise things that are about to become large problems but right now are just small irritations. Secondly, it has helped instil a mindset in which the whole team is responsible for improving the way we work and given us a sense of common ownership for our team process. It is taking us closer to that ever elusive goal: the self-managed team.

Try…

So does your team have a structure for regularly taking time together to look backwards to work out how best to go forwards? Do you like the idea of continuously improving the way the team is working but you don’t know how to approach this? Why not try running retrospectives with your team?

Technical debt is one of the great “inventions” in software engineering of the 1990s. The idea, if you are not familiar with it, is that every time you cut a corner in the interest of delivering value now i.e. delivering on the immediate needs of the project, you record it as a debt. The reason it is a debt, is that if you don’t pay it back then it will impede your ability to deliver value in the future. Examples of technical debt include:

Incurring technical debt can be the right thing to do, providing its incurred in a prudent and deliberate manner.

A fascinating reason for taking debt repayments seriously is the broken window theory. The theory has it’s roots in experiments made in the 80’s. Experiments showed that disorder and crime are linked – if a windows in a building is broken and left unrepaired, all the rest will soon be broken. People seemingly get the message that nobody cares. The insanely complex nature of software systems means that similar behaviour is observed – nothing accelerates the speed of the descent into un-maintainability than allowing technical debt to build up.

Process-wise, what is required is:

A process for recording technical debt and making it visible to stakeholders

A process for deciding when to paying it back

The first bit is easy. The second is tricky. The solution for the second bit that some teams I have worked with hasve settled on allocating 50% of the development effort to paying down technical debt. Why 50%? Why not 40% or 60%. I’m guessing that 50% was the maximum the team felt they could ask for without having an unacceptable impact on the delivery of value now.

A better approach would be to use an economics-based prioritisation algorithm (like cost-of-delay divided by duration). Its not easy, since we would need to estimate the benefits/savings from paying down each element of the debt, but it’s worth it. An effective technical debt process avoids creating solutions that, just a few years after they were first created, we can no longer effectively change because the cost and risk is too high.

Here’s a interesting real-life story of a requirement from one of the delivery teams that I have worked with which emphasises how a focus on cost-of-delay helps.

The story concerns requirement RQ-0672 which took 46 weeks to go from being captured as a idea to being live in production. We have a fair idea of which weeks there were activities happening (green) and which weeks nothing happened (red); shown in the value stream map below (See here to learn more about value stream mapping techniques):

Observation 1: It’s pretty clear there was a lot of “waiting waste”. Most weeks, nothing happened!

We introduced the concept of cost-of-delay to this team – i.e. measuring the opportunity cost to the company for every week this requirement was not implemented. This turned out to be $214,000 per week. Cost-of-delay figures often have high levels of uncertainty (particularly if the benefits of the requirements are around increasing revenue as in this case) but in this case, this number is fairly certain.

This particular delivery stream is capable of delivering a requirement like this within 13 weeks. Had this been delivered within 13 weeks instead of 46 i.e. 33 weeks earlier, this would have improved the company’s revenue by:

$214,000 x 33 = $7,062,000

Wow. Big number, particularly in the content the actual cost of making the change was around $4,200.

Obseveration 2: There can be significant value in reducing cycle time.

So why didn’t we make it happen earlier? The pipeline was setup without a focus on cost-of-delay which means there is no sense of the urgency associated with each requirement – they are all treated the same. Once we know the urgency, then we know what trade-offs to make.

In this case, you could say that it makes sense for the company overall to spend up to $214,000 for every week we could bring forward the go-live date for this requirement. You can also see from the value stream mapping that there was a proof-of-concept done for this requirement. This proof-of-concept took a week to execute – plus some mobilisation time – probably several weeks – hard to know exactly. Lets say 2 weeks mobilisation for the sake of argument. The cost of delaying the go-live by 3 weeks to do the proof-of-concept was:

3 x $214,000 = $642,000

The purpose of this proof-of-concept was to be able to accurately estimate the cost of the change. Remember, this change actually came in at a cost of $4,200. Given the enormous benefits, it would still have made sense for the company to implement this change if the cost had come in at $4,200,000. Perhaps we should have skipped the proof-of-concept stage for this requirement!

Observation 3: Not all estimating activity is truly value-adding

I have selected an extreme example of course. Even so, its real. It happened. It serves to emphasis that some requirements have high cost-of-delay which needs to be managed. A good innovation process design should expedite these types of requirements by:

Prioritising quickly based on cost-of-delay (ideally within a week)

A frequent “pull” of the top requirement from the top of the dynamic prioritised list of requirement

Fast cycle time from the time of “pull” to go-live through a pipeline which is not clogged up with too many other requirements

An interesting area of discussion is to whether a lean-agile approach contributes anything to effective risk management. Do lean-agile practices effectively mitigate risk? Some say yes and some say no.

No!

There are, I believe, currently no lean-agile practices which are designed to identify the most important risks and then focus on mitigating and tracking them. Other (traditional) project management approaches have tools and practices for this – for example by the creation and ongoing review of a risk log. Yet this is missing in the lean-agile toolkit. Lean-agile practices need supplementing by a traditional risk management practice.

Yes!

The traditional risk log approach is of limited effectiveness. If you ask a team what the top three risks the team faces are (go on, try it), it’s rare that the team comes with a consistent answer. Even when a risk log helps identify risks, analysing the true root cause of a risk in any particular case can be difficult.

By implementing lean-agile practices, teams get access to ways of mitigating the highest risks that an IT development project faces as evaluated by broad industry experience.

Take the Agile Manifesto. It was created by a group of experienced people. Apparently, it was tough to find things they agreed on (even though they were experienced, the number of projects they have worked on is still small so the number of data points they have is also small). They did manage to agree on the agile principles behind the manifesto. Each principle addresses one or more key underlying risk facing an IT development project. I’ve had a go at identifying the implied risk below. Would you think of these when you start a IT development project? Would you agree that these are the key risks?

Principle

Implied risk

Our highest priority is to satisfy the customer
through early and continuous delivery
of valuable software.

Solution deliver generates no or little value. Unused features.

Deliver working software frequently, from a
couple of weeks to a couple of months, with a
preference to the shorter timescale.

Solution could not be built as intended. Solution delivers no value. Things external to the project change.

Business people and developers must work
together daily throughout the project.

Business people and developers do not communicate quickly & effectively.

Build projects around motivated individuals.
Give them the environment and support they need,
and trust them to get the job done

Environments and support not available.
Teams not empowered.

The most efficient and effective method of
conveying information to and within a development
team is face-to-face conversation.

Geographical distribution of teams and over-reliance on electronic communication leads to ineffective communication.

Working software is the primary measure of progress.

Focus on internal goals instead of meaningful ones (delivery of working software)

Agile processes promote sustainable development.
The sponsors, developers, and users should be able
to maintain a constant pace indefinitely.

Unsustainable development (pace, architecture, …)

Continuous attention to technical excellence
and good design enhances agility.

Lack of focus on technical excellence.

Simplicity–the art of maximizing the amount
of work not done–is essential.

Complex code. Doing work that doesn’t need to be done.

The best architectures, requirements, and designs
emerge from self-organizing teams.

Ineffective teams due to poor team structure/task management and the team not being empowered to change this.